Contrastive Learning for Network Intrusion Detection: A Comprehensive Survey

With the increasing complexity and variety of network attacks, traditional rule-based and supervised network intrusion detection systems (NIDS) face significant limitations, especially in detecting zero-day and unknown threats.Contrastive learning-based NIDS utilizes advanced techniques to learn network traffic representations and identify abnormal behaviors, providing strong adaptability and robustness.This method constructs positive and negative sample pairs to optimize the model, maximize the feature distance between similar samples and minimize the distance between dissimilar samples, so that the model can effectively learn the deep representation of network traffic.Unlike traditional methods, contrastive learning-based NIDS does not heavily rely on large amounts of labeled data, making it adaptable to changing attack patterns.Experimental results show that the NIDS based on contrastive learning is superior to traditional methods in detecting complex attack types and unknown threats, and has obvious advantages in zero-day attack detection and large-scale data processing.However, challenges remain, including sample construction and computational resource requirements.Future research could explore strategies to optimize sample selection and training efficiency to further improve the accuracy and practical applicability of the model.